Supply chains aid the manufacturing of many complex goods.
Traditionally, supply chains have been maintained by human negotiators
through long-term, static contracts, despite uncertain and dynamic
market conditions. However, there has been a recent growing interest,
from both industry and academia, in the potential for automating more
efficient supply chain processes. The TAC SCM (Trading Agent
Comeptition in Supply Chain Management) scenario is an international
competition that provides a research platform facilitating the
application of new academic technologies to the problem of managing a
dynamic supply chain. Since the inception of TAC SCM, machine learning
has emerged an essential aspect of successful agent design. Many
agents, such as Carnegie Mellon¿s 2005 entry, CMieux, utilize learning
techniques to estimate market conditions, and model opponent behavior.
In this talk, we will discuss some specific learning problems faced by
these agents, including the problem of forecasting future demand, the
problem of predicting auction closing prices, and the problem of
approximating supply availability. We will also discuss various
solutions developed by researchers to address them, including a new
extension of M5 regression trees used by CMieux, called distribution
trees.